Overview

Dataset statistics

Number of variables24
Number of observations8
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory208.0 B

Variable types

Categorical1
Numeric23

Alerts

Tkl is highly overall correlated with TklW and 11 other fieldsHigh correlation
TklW is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Def 3rd is highly overall correlated with Tkl and 16 other fieldsHigh correlation
Mid 3rd is highly overall correlated with Sh and 3 other fieldsHigh correlation
Att 3rd is highly overall correlated with Tkl.1 and 5 other fieldsHigh correlation
Tkl.1 is highly overall correlated with Tkl and 12 other fieldsHigh correlation
Att is highly overall correlated with Tkl and 16 other fieldsHigh correlation
Tkl% is highly overall correlated with Att 3rd and 3 other fieldsHigh correlation
Lost is highly overall correlated with Tkl and 15 other fieldsHigh correlation
Blocks is highly overall correlated with Tkl and 17 other fieldsHigh correlation
Sh is highly overall correlated with Mid 3rd and 9 other fieldsHigh correlation
Pass is highly overall correlated with Tkl and 8 other fieldsHigh correlation
Int is highly overall correlated with PlayerHigh correlation
Clr is highly overall correlated with Def 3rd and 14 other fieldsHigh correlation
DefPen is highly overall correlated with Tkl and 14 other fieldsHigh correlation
Def3rdT is highly overall correlated with Def 3rd and 11 other fieldsHigh correlation
Mid3rdT is highly overall correlated with Def 3rd and 14 other fieldsHigh correlation
Att3rdT is highly overall correlated with Def 3rd and 14 other fieldsHigh correlation
AttPen is highly overall correlated with Tkl and 16 other fieldsHigh correlation
DeadBall is highly overall correlated with PlayerHigh correlation
AerialDuel is highly overall correlated with Def 3rd and 12 other fieldsHigh correlation
Goals is highly overall correlated with Tkl and 14 other fieldsHigh correlation
xG is highly overall correlated with Tkl and 16 other fieldsHigh correlation
Player is highly overall correlated with Tkl and 22 other fieldsHigh correlation
Player is uniformly distributedUniform
Clr is uniformly distributedUniform
Player has unique valuesUnique
Tkl has unique valuesUnique
TklW has unique valuesUnique
Def 3rd has unique valuesUnique
Mid 3rd has unique valuesUnique
Att 3rd has unique valuesUnique
Tkl.1 has unique valuesUnique
Att has unique valuesUnique
Tkl% has unique valuesUnique
Lost has unique valuesUnique
Blocks has unique valuesUnique
Sh has unique valuesUnique
Pass has unique valuesUnique
Int has unique valuesUnique
Clr has unique valuesUnique
DefPen has unique valuesUnique
Def3rdT has unique valuesUnique
Mid3rdT has unique valuesUnique
Att3rdT has unique valuesUnique
AttPen has unique valuesUnique
DeadBall has unique valuesUnique
AerialDuel has unique valuesUnique
Goals has unique valuesUnique
xG has unique valuesUnique

Reproduction

Analysis started2023-02-12 15:31:37.084661
Analysis finished2023-02-12 15:32:25.492829
Duration48.41 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Player
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size192.0 B
England
France
Germany
Italy
Netherlands
Other values (3)

Length

Max length11
Median length7
Mean length6.375
Min length3

Characters and Unicode

Total characters51
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st rowEngland
2nd rowFrance
3rd rowGermany
4th rowItaly
5th rowNetherlands

Common Values

ValueCountFrequency (%)
England 1
12.5%
France 1
12.5%
Germany 1
12.5%
Italy 1
12.5%
Netherlands 1
12.5%
Norway 1
12.5%
Sweden 1
12.5%
USA 1
12.5%

Length

2023-02-12T10:32:25.560844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-12T10:32:25.676870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
england 1
12.5%
france 1
12.5%
germany 1
12.5%
italy 1
12.5%
netherlands 1
12.5%
norway 1
12.5%
sweden 1
12.5%
usa 1
12.5%

Most occurring characters

ValueCountFrequency (%)
a 6
11.8%
e 6
11.8%
n 6
11.8%
r 4
 
7.8%
l 3
 
5.9%
d 3
 
5.9%
y 3
 
5.9%
S 2
 
3.9%
w 2
 
3.9%
t 2
 
3.9%
Other values (13) 14
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41
80.4%
Uppercase Letter 10
 
19.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6
14.6%
e 6
14.6%
n 6
14.6%
r 4
9.8%
l 3
7.3%
d 3
7.3%
y 3
7.3%
w 2
 
4.9%
t 2
 
4.9%
h 1
 
2.4%
Other values (5) 5
12.2%
Uppercase Letter
ValueCountFrequency (%)
S 2
20.0%
N 2
20.0%
E 1
10.0%
U 1
10.0%
I 1
10.0%
G 1
10.0%
F 1
10.0%
A 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6
11.8%
e 6
11.8%
n 6
11.8%
r 4
 
7.8%
l 3
 
5.9%
d 3
 
5.9%
y 3
 
5.9%
S 2
 
3.9%
w 2
 
3.9%
t 2
 
3.9%
Other values (13) 14
27.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6
11.8%
e 6
11.8%
n 6
11.8%
r 4
 
7.8%
l 3
 
5.9%
d 3
 
5.9%
y 3
 
5.9%
S 2
 
3.9%
w 2
 
3.9%
t 2
 
3.9%
Other values (13) 14
27.5%

Tkl
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96445052
Minimum0.65363129
Maximum1.4197531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:25.780893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.65363129
5-th percentile0.6824446
Q10.84268442
median0.8995614
Q31.084564
95-th percentile1.3338072
Maximum1.4197531
Range0.7661218
Interquartile range (IQR)0.2418796

Descriptive statistics

Standard deviation0.24629558
Coefficient of variation (CV)0.255374
Kurtosis0.42370459
Mean0.96445052
Median Absolute Deviation (MAD)0.15936622
Skewness0.77894659
Sum7.7156042
Variance0.060661515
MonotonicityNot monotonic
2023-02-12T10:32:25.862911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.0546875 1
12.5%
0.883333333 1
12.5%
0.87826087 1
12.5%
1.419753086 1
12.5%
1.174193548 1
12.5%
0.915789474 1
12.5%
0.735955056 1
12.5%
0.653631285 1
12.5%
ValueCountFrequency (%)
0.653631285 1
12.5%
0.735955056 1
12.5%
0.87826087 1
12.5%
0.883333333 1
12.5%
0.915789474 1
12.5%
1.0546875 1
12.5%
1.174193548 1
12.5%
1.419753086 1
12.5%
ValueCountFrequency (%)
1.419753086 1
12.5%
1.174193548 1
12.5%
1.0546875 1
12.5%
0.915789474 1
12.5%
0.883333333 1
12.5%
0.87826087 1
12.5%
0.735955056 1
12.5%
0.653631285 1
12.5%

TklW
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95646555
Minimum0.68493151
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:25.951931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.68493151
5-th percentile0.68895548
Q10.71822479
median0.89481611
Q31.0226815
95-th percentile1.455625
Maximum1.6
Range0.91506849
Interquartile range (IQR)0.30445666

Descriptive statistics

Standard deviation0.31007957
Coefficient of variation (CV)0.32419314
Kurtosis2.047025
Mean0.95646555
Median Absolute Deviation (MAD)0.18385672
Skewness1.4558573
Sum7.6517244
Variance0.096149342
MonotonicityNot monotonic
2023-02-12T10:32:26.033950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.934210526 1
12.5%
0.855421687 1
12.5%
0.684931507 1
12.5%
1.6 1
12.5%
1.1875 1
12.5%
0.967741935 1
12.5%
0.696428571 1
12.5%
0.725490196 1
12.5%
ValueCountFrequency (%)
0.684931507 1
12.5%
0.696428571 1
12.5%
0.725490196 1
12.5%
0.855421687 1
12.5%
0.934210526 1
12.5%
0.967741935 1
12.5%
1.1875 1
12.5%
1.6 1
12.5%
ValueCountFrequency (%)
1.6 1
12.5%
1.1875 1
12.5%
0.967741935 1
12.5%
0.934210526 1
12.5%
0.855421687 1
12.5%
0.725490196 1
12.5%
0.696428571 1
12.5%
0.684931507 1
12.5%

Def 3rd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87303506
Minimum0.46226415
Maximum1.8888889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.114968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.46226415
5-th percentile0.46713836
Q10.48623512
median0.84367682
Q30.985
95-th percentile1.5777778
Maximum1.8888889
Range1.4266247
Interquartile range (IQR)0.49876488

Descriptive statistics

Standard deviation0.46922692
Coefficient of variation (CV)0.53746629
Kurtosis3.1647949
Mean0.87303506
Median Absolute Deviation (MAD)0.25520833
Skewness1.5906747
Sum6.9842805
Variance0.2201739
MonotonicityNot monotonic
2023-02-12T10:32:26.195986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.901639344 1
12.5%
0.476190476 1
12.5%
0.785714286 1
12.5%
1.888888889 1
12.5%
1 1
12.5%
0.98 1
12.5%
0.489583333 1
12.5%
0.462264151 1
12.5%
ValueCountFrequency (%)
0.462264151 1
12.5%
0.476190476 1
12.5%
0.489583333 1
12.5%
0.785714286 1
12.5%
0.901639344 1
12.5%
0.98 1
12.5%
1 1
12.5%
1.888888889 1
12.5%
ValueCountFrequency (%)
1.888888889 1
12.5%
1 1
12.5%
0.98 1
12.5%
0.901639344 1
12.5%
0.785714286 1
12.5%
0.489583333 1
12.5%
0.476190476 1
12.5%
0.462264151 1
12.5%

Mid 3rd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1062136
Minimum0.69230769
Maximum1.6896552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.284007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.69230769
5-th percentile0.74354839
Q10.84646987
median1.0662862
Q31.2818519
95-th percentile1.5908685
Maximum1.6896552
Range0.99734748
Interquartile range (IQR)0.43538198

Descriptive statistics

Standard deviation0.3309059
Coefficient of variation (CV)0.29913382
Kurtosis-0.22929326
Mean1.1062136
Median Absolute Deviation (MAD)0.22240308
Skewness0.63349144
Sum8.849709
Variance0.10949872
MonotonicityNot monotonic
2023-02-12T10:32:26.362024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.24 1
12.5%
1.689655172 1
12.5%
0.849056604 1
12.5%
1.117647059 1
12.5%
1.407407407 1
12.5%
0.692307692 1
12.5%
1.014925373 1
12.5%
0.838709677 1
12.5%
ValueCountFrequency (%)
0.692307692 1
12.5%
0.838709677 1
12.5%
0.849056604 1
12.5%
1.014925373 1
12.5%
1.117647059 1
12.5%
1.24 1
12.5%
1.407407407 1
12.5%
1.689655172 1
12.5%
ValueCountFrequency (%)
1.689655172 1
12.5%
1.407407407 1
12.5%
1.24 1
12.5%
1.117647059 1
12.5%
1.014925373 1
12.5%
0.849056604 1
12.5%
0.838709677 1
12.5%
0.692307692 1
12.5%

Att 3rd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5200153
Minimum0.81818182
Maximum2.4285714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.441043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.81818182
5-th percentile0.90240642
Q11.0647059
median1.4772727
Q31.875
95-th percentile2.2785714
Maximum2.4285714
Range1.6103896
Interquartile range (IQR)0.81029412

Descriptive statistics

Standard deviation0.54373152
Coefficient of variation (CV)0.35771451
Kurtosis-0.68614751
Mean1.5200153
Median Absolute Deviation (MAD)0.41452763
Skewness0.4195419
Sum12.160122
Variance0.29564396
MonotonicityNot monotonic
2023-02-12T10:32:26.519059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.058823529 1
12.5%
2.428571429 1
12.5%
2 1
12.5%
0.818181818 1
12.5%
1.5 1
12.5%
1.833333333 1
12.5%
1.066666667 1
12.5%
1.454545455 1
12.5%
ValueCountFrequency (%)
0.818181818 1
12.5%
1.058823529 1
12.5%
1.066666667 1
12.5%
1.454545455 1
12.5%
1.5 1
12.5%
1.833333333 1
12.5%
2 1
12.5%
2.428571429 1
12.5%
ValueCountFrequency (%)
2.428571429 1
12.5%
2 1
12.5%
1.833333333 1
12.5%
1.5 1
12.5%
1.454545455 1
12.5%
1.066666667 1
12.5%
1.058823529 1
12.5%
0.818181818 1
12.5%

Tkl.1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86193198
Minimum0.56818182
Maximum1.6896552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.610081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.56818182
5-th percentile0.57475296
Q10.5879156
median0.75243507
Q30.95367495
95-th percentile1.4584208
Maximum1.6896552
Range1.1214734
Interquartile range (IQR)0.36575935

Descriptive statistics

Standard deviation0.37399133
Coefficient of variation (CV)0.43389889
Kurtosis3.6754622
Mean0.86193198
Median Absolute Deviation (MAD)0.17080745
Skewness1.8298054
Sum6.8954559
Variance0.13986952
MonotonicityNot monotonic
2023-02-12T10:32:26.690099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.928571429 1
12.5%
0.586956522 1
12.5%
0.568181818 1
12.5%
1.689655172 1
12.5%
1.028985507 1
12.5%
0.772727273 1
12.5%
0.588235294 1
12.5%
0.732142857 1
12.5%
ValueCountFrequency (%)
0.568181818 1
12.5%
0.586956522 1
12.5%
0.588235294 1
12.5%
0.732142857 1
12.5%
0.772727273 1
12.5%
0.928571429 1
12.5%
1.028985507 1
12.5%
1.689655172 1
12.5%
ValueCountFrequency (%)
1.689655172 1
12.5%
1.028985507 1
12.5%
0.928571429 1
12.5%
0.772727273 1
12.5%
0.732142857 1
12.5%
0.588235294 1
12.5%
0.586956522 1
12.5%
0.568181818 1
12.5%

Att
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83636348
Minimum0.55319149
Maximum1.4375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.779118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.55319149
5-th percentile0.56531771
Q10.62343005
median0.70961538
Q30.94739024
95-th percentile1.351575
Maximum1.4375
Range0.88430851
Interquartile range (IQR)0.3239602

Descriptive statistics

Standard deviation0.31768906
Coefficient of variation (CV)0.37984568
Kurtosis0.46182034
Mean0.83636348
Median Absolute Deviation (MAD)0.13900791
Skewness1.251914
Sum6.6909079
Variance0.10092634
MonotonicityNot monotonic
2023-02-12T10:32:26.855135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.75 1
12.5%
0.553191489 1
12.5%
0.635294118 1
12.5%
1.4375 1
12.5%
1.192 1
12.5%
0.865853659 1
12.5%
0.587837838 1
12.5%
0.669230769 1
12.5%
ValueCountFrequency (%)
0.553191489 1
12.5%
0.587837838 1
12.5%
0.635294118 1
12.5%
0.669230769 1
12.5%
0.75 1
12.5%
0.865853659 1
12.5%
1.192 1
12.5%
1.4375 1
12.5%
ValueCountFrequency (%)
1.4375 1
12.5%
1.192 1
12.5%
0.865853659 1
12.5%
0.75 1
12.5%
0.669230769 1
12.5%
0.635294118 1
12.5%
0.587837838 1
12.5%
0.553191489 1
12.5%

Tkl%
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0275018
Minimum0.86413043
Maximum1.2375691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:26.936154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.86413043
5-th percentile0.87388218
Q10.89336493
median1.0315459
Q31.1137557
95-th percentile1.21623
Maximum1.2375691
Range0.37343863
Interquartile range (IQR)0.22039075

Descriptive statistics

Standard deviation0.13905593
Coefficient of variation (CV)0.13533401
Kurtosis-1.3876959
Mean1.0275018
Median Absolute Deviation (MAD)0.13863845
Skewness0.2583515
Sum8.2200142
Variance0.019336552
MonotonicityNot monotonic
2023-02-12T10:32:27.017172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.237569061 1
12.5%
1.061349693 1
12.5%
0.893822394 1
12.5%
1.176600442 1
12.5%
0.864130435 1
12.5%
0.891992551 1
12.5%
1.00174216 1
12.5%
1.092807425 1
12.5%
ValueCountFrequency (%)
0.864130435 1
12.5%
0.891992551 1
12.5%
0.893822394 1
12.5%
1.00174216 1
12.5%
1.061349693 1
12.5%
1.092807425 1
12.5%
1.176600442 1
12.5%
1.237569061 1
12.5%
ValueCountFrequency (%)
1.237569061 1
12.5%
1.176600442 1
12.5%
1.092807425 1
12.5%
1.061349693 1
12.5%
1.00174216 1
12.5%
0.893822394 1
12.5%
0.891992551 1
12.5%
0.864130435 1
12.5%

Lost
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83510438
Minimum0.52083333
Maximum1.3928571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:27.109193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.52083333
5-th percentile0.54409722
Q10.61304161
median0.67798286
Q31.037406
95-th percentile1.3353571
Maximum1.3928571
Range0.87202381
Interquartile range (IQR)0.4243644

Descriptive statistics

Standard deviation0.32556579
Coefficient of variation (CV)0.38985041
Kurtosis-0.68267829
Mean0.83510438
Median Absolute Deviation (MAD)0.1239154
Skewness0.95234598
Sum6.680835
Variance0.10599308
MonotonicityNot monotonic
2023-02-12T10:32:27.186210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.648648649 1
12.5%
0.520833333 1
12.5%
0.707317073 1
12.5%
1.228571429 1
12.5%
1.392857143 1
12.5%
0.973684211 1
12.5%
0.587301587 1
12.5%
0.621621622 1
12.5%
ValueCountFrequency (%)
0.520833333 1
12.5%
0.587301587 1
12.5%
0.621621622 1
12.5%
0.648648649 1
12.5%
0.707317073 1
12.5%
0.973684211 1
12.5%
1.228571429 1
12.5%
1.392857143 1
12.5%
ValueCountFrequency (%)
1.392857143 1
12.5%
1.228571429 1
12.5%
0.973684211 1
12.5%
0.707317073 1
12.5%
0.648648649 1
12.5%
0.621621622 1
12.5%
0.587301587 1
12.5%
0.520833333 1
12.5%

Blocks
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92653818
Minimum0.54320988
Maximum1.4528302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:27.267228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.54320988
5-th percentile0.63407234
Q10.82394366
median0.88852814
Q30.95918289
95-th percentile1.3257499
Maximum1.4528302
Range0.90962031
Interquartile range (IQR)0.13523922

Descriptive statistics

Standard deviation0.26134806
Coefficient of variation (CV)0.28206939
Kurtosis2.4008935
Mean0.92653818
Median Absolute Deviation (MAD)0.07162673
Skewness0.96999041
Sum7.4123054
Variance0.068302807
MonotonicityNot monotonic
2023-02-12T10:32:27.349247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.880952381 1
12.5%
0.543209877 1
12.5%
0.802816901 1
12.5%
1.452830189 1
12.5%
1.08974359 1
12.5%
0.896103896 1
12.5%
0.915662651 1
12.5%
0.830985915 1
12.5%
ValueCountFrequency (%)
0.543209877 1
12.5%
0.802816901 1
12.5%
0.830985915 1
12.5%
0.880952381 1
12.5%
0.896103896 1
12.5%
0.915662651 1
12.5%
1.08974359 1
12.5%
1.452830189 1
12.5%
ValueCountFrequency (%)
1.452830189 1
12.5%
1.08974359 1
12.5%
0.915662651 1
12.5%
0.896103896 1
12.5%
0.880952381 1
12.5%
0.830985915 1
12.5%
0.802816901 1
12.5%
0.543209877 1
12.5%

Sh
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96583508
Minimum0.29166667
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:27.431265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.29166667
5-th percentile0.33958333
Q10.55714286
median0.87941176
Q31.2440476
95-th percentile1.8316667
Maximum2.1
Range1.8083333
Interquartile range (IQR)0.68690476

Descriptive statistics

Standard deviation0.59032902
Coefficient of variation (CV)0.61121099
Kurtosis0.7168867
Mean0.96583508
Median Absolute Deviation (MAD)0.39285714
Skewness0.921667
Sum7.7266807
Variance0.34848835
MonotonicityNot monotonic
2023-02-12T10:32:27.512283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.428571429 1
12.5%
0.291666667 1
12.5%
1.058823529 1
12.5%
2.1 1
12.5%
0.6 1
12.5%
1.333333333 1
12.5%
1.214285714 1
12.5%
0.7 1
12.5%
ValueCountFrequency (%)
0.291666667 1
12.5%
0.428571429 1
12.5%
0.6 1
12.5%
0.7 1
12.5%
1.058823529 1
12.5%
1.214285714 1
12.5%
1.333333333 1
12.5%
2.1 1
12.5%
ValueCountFrequency (%)
2.1 1
12.5%
1.333333333 1
12.5%
1.214285714 1
12.5%
1.058823529 1
12.5%
0.7 1
12.5%
0.6 1
12.5%
0.428571429 1
12.5%
0.291666667 1
12.5%

Pass
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94078858
Minimum0.64912281
Maximum1.3207547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:27.596304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.64912281
5-th percentile0.6747076
Q10.75258945
median0.8687127
Q31.0993909
95-th percentile1.3143045
Maximum1.3207547
Range0.67163191
Interquartile range (IQR)0.34680147

Descriptive statistics

Standard deviation0.25597712
Coefficient of variation (CV)0.27208783
Kurtosis-1.0200044
Mean0.94078858
Median Absolute Deviation (MAD)0.15476191
Skewness0.69428781
Sum7.5263086
Variance0.065524285
MonotonicityNot monotonic
2023-02-12T10:32:27.670319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.031746032 1
12.5%
0.649122807 1
12.5%
0.722222222 1
12.5%
1.302325581 1
12.5%
1.320754717 1
12.5%
0.762711864 1
12.5%
0.855072464 1
12.5%
0.882352941 1
12.5%
ValueCountFrequency (%)
0.649122807 1
12.5%
0.722222222 1
12.5%
0.762711864 1
12.5%
0.855072464 1
12.5%
0.882352941 1
12.5%
1.031746032 1
12.5%
1.302325581 1
12.5%
1.320754717 1
12.5%
ValueCountFrequency (%)
1.320754717 1
12.5%
1.302325581 1
12.5%
1.031746032 1
12.5%
0.882352941 1
12.5%
0.855072464 1
12.5%
0.762711864 1
12.5%
0.722222222 1
12.5%
0.649122807 1
12.5%

Int
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84643334
Minimum0.69473684
Maximum1.1529412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:27.750337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.69473684
5-th percentile0.71277298
Q10.75721934
median0.80902291
Q30.88965116
95-th percentile1.0709234
Maximum1.1529412
Range0.45820433
Interquartile range (IQR)0.13243182

Descriptive statistics

Standard deviation0.14315862
Coefficient of variation (CV)0.16913159
Kurtosis2.8892466
Mean0.84643334
Median Absolute Deviation (MAD)0.066865672
Skewness1.5568722
Sum6.7714667
Variance0.02049439
MonotonicityNot monotonic
2023-02-12T10:32:27.977389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.760869565 1
12.5%
0.811594203 1
12.5%
0.806451613 1
12.5%
0.88 1
12.5%
1.152941176 1
12.5%
0.746268657 1
12.5%
0.694736842 1
12.5%
0.918604651 1
12.5%
ValueCountFrequency (%)
0.694736842 1
12.5%
0.746268657 1
12.5%
0.760869565 1
12.5%
0.806451613 1
12.5%
0.811594203 1
12.5%
0.88 1
12.5%
0.918604651 1
12.5%
1.152941176 1
12.5%
ValueCountFrequency (%)
1.152941176 1
12.5%
0.918604651 1
12.5%
0.88 1
12.5%
0.811594203 1
12.5%
0.806451613 1
12.5%
0.760869565 1
12.5%
0.746268657 1
12.5%
0.694736842 1
12.5%

Clr
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85697149
Minimum0.37062937
Maximum1.3555556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.055406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.37062937
5-th percentile0.42732213
Q10.6297738
median0.84820303
Q31.0334587
95-th percentile1.3328553
Maximum1.3555556
Range0.98492619
Interquartile range (IQR)0.40368494

Descriptive statistics

Standard deviation0.34692405
Coefficient of variation (CV)0.40482566
Kurtosis-0.97401845
Mean0.85697149
Median Absolute Deviation (MAD)0.2508176
Skewness0.22471138
Sum6.8557719
Variance0.1203563
MonotonicityNot monotonic
2023-02-12T10:32:28.136424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.662162162 1
12.5%
0.370629371 1
12.5%
0.754098361 1
12.5%
1.355555556 1
12.5%
0.942307692 1
12.5%
1.290697674 1
12.5%
0.947712418 1
12.5%
0.532608696 1
12.5%
ValueCountFrequency (%)
0.370629371 1
12.5%
0.532608696 1
12.5%
0.662162162 1
12.5%
0.754098361 1
12.5%
0.942307692 1
12.5%
0.947712418 1
12.5%
1.290697674 1
12.5%
1.355555556 1
12.5%
ValueCountFrequency (%)
1.355555556 1
12.5%
1.290697674 1
12.5%
0.947712418 1
12.5%
0.942307692 1
12.5%
0.754098361 1
12.5%
0.662162162 1
12.5%
0.532608696 1
12.5%
0.370629371 1
12.5%

DefPen
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88904461
Minimum0.421875
Maximum1.4456929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.216443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.421875
5-th percentile0.48312744
Q10.7316673
median0.8196121
Q31.0122471
95-th percentile1.4029731
Maximum1.4456929
Range1.0238179
Interquartile range (IQR)0.28057982

Descriptive statistics

Standard deviation0.34359151
Coefficient of variation (CV)0.38647274
Kurtosis-0.30342106
Mean0.88904461
Median Absolute Deviation (MAD)0.15578437
Skewness0.58835245
Sum7.1123569
Variance0.11805513
MonotonicityNot monotonic
2023-02-12T10:32:28.298461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.793258427 1
12.5%
0.421875 1
12.5%
0.908450704 1
12.5%
1.323636364 1
12.5%
0.84596577 1
12.5%
1.445692884 1
12.5%
0.776595745 1
12.5%
0.59688196 1
12.5%
ValueCountFrequency (%)
0.421875 1
12.5%
0.59688196 1
12.5%
0.776595745 1
12.5%
0.793258427 1
12.5%
0.84596577 1
12.5%
0.908450704 1
12.5%
1.323636364 1
12.5%
1.445692884 1
12.5%
ValueCountFrequency (%)
1.445692884 1
12.5%
1.323636364 1
12.5%
0.908450704 1
12.5%
0.84596577 1
12.5%
0.793258427 1
12.5%
0.776595745 1
12.5%
0.59688196 1
12.5%
0.421875 1
12.5%

Def3rdT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93965989
Minimum0.55932203
Maximum1.3606557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.381480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.55932203
5-th percentile0.60381356
Q10.72748004
median0.99542102
Q31.0784684
95-th percentile1.2733992
Maximum1.3606557
Range0.8013337
Interquartile range (IQR)0.35098836

Descriptive statistics

Standard deviation0.26132176
Coefficient of variation (CV)0.2781025
Kurtosis-0.5498552
Mean0.93965989
Median Absolute Deviation (MAD)0.18509576
Skewness0.033431022
Sum7.5172791
Variance0.068289062
MonotonicityNot monotonic
2023-02-12T10:32:28.463498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.978609626 1
12.5%
0.559322034 1
12.5%
1.111351351 1
12.5%
1.012232416 1
12.5%
1.067507418 1
12.5%
1.360655738 1
12.5%
0.686440678 1
12.5%
0.74115983 1
12.5%
ValueCountFrequency (%)
0.559322034 1
12.5%
0.686440678 1
12.5%
0.74115983 1
12.5%
0.978609626 1
12.5%
1.012232416 1
12.5%
1.067507418 1
12.5%
1.111351351 1
12.5%
1.360655738 1
12.5%
ValueCountFrequency (%)
1.360655738 1
12.5%
1.111351351 1
12.5%
1.067507418 1
12.5%
1.012232416 1
12.5%
0.978609626 1
12.5%
0.74115983 1
12.5%
0.686440678 1
12.5%
0.559322034 1
12.5%

Mid3rdT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2124569
Minimum0.80914827
Maximum1.6760417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.552518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.80914827
5-th percentile0.85523783
Q10.97071174
median1.2144805
Q31.3841987
95-th percentile1.6240883
Maximum1.6760417
Range0.8668934
Interquartile range (IQR)0.41348693

Descriptive statistics

Standard deviation0.29989754
Coefficient of variation (CV)0.24734696
Kurtosis-1.0376986
Mean1.2124569
Median Absolute Deviation (MAD)0.2537284
Skewness0.24574058
Sum9.6996556
Variance0.089938532
MonotonicityNot monotonic
2023-02-12T10:32:28.629536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.527603513 1
12.5%
1.676041667 1
12.5%
1.144444444 1
12.5%
0.809148265 1
12.5%
1.28451649 1
12.5%
0.940832725 1
12.5%
0.980671414 1
12.5%
1.336397059 1
12.5%
ValueCountFrequency (%)
0.809148265 1
12.5%
0.940832725 1
12.5%
0.980671414 1
12.5%
1.144444444 1
12.5%
1.28451649 1
12.5%
1.336397059 1
12.5%
1.527603513 1
12.5%
1.676041667 1
12.5%
ValueCountFrequency (%)
1.676041667 1
12.5%
1.527603513 1
12.5%
1.336397059 1
12.5%
1.28451649 1
12.5%
1.144444444 1
12.5%
0.980671414 1
12.5%
0.940832725 1
12.5%
0.809148265 1
12.5%

Att3rdT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6009516
Minimum0.57687577
Maximum3.2662338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.707555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.57687577
5-th percentile0.63184189
Q11.1484917
median1.6020456
Q31.7637606
95-th percentile2.8518191
Maximum3.2662338
Range2.689358
Interquartile range (IQR)0.61526887

Descriptive statistics

Standard deviation0.83764383
Coefficient of variation (CV)0.52321621
Kurtosis1.659509
Mean1.6009516
Median Absolute Deviation (MAD)0.39775503
Skewness0.96349587
Sum12.807613
Variance0.70164719
MonotonicityNot monotonic
2023-02-12T10:32:28.791574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.657616893 1
12.5%
3.266233766 1
12.5%
1.648535565 1
12.5%
0.576875769 1
12.5%
1.555555556 1
12.5%
0.733921816 1
12.5%
1.286681716 1
12.5%
2.082191781 1
12.5%
ValueCountFrequency (%)
0.576875769 1
12.5%
0.733921816 1
12.5%
1.286681716 1
12.5%
1.555555556 1
12.5%
1.648535565 1
12.5%
1.657616893 1
12.5%
2.082191781 1
12.5%
3.266233766 1
12.5%
ValueCountFrequency (%)
3.266233766 1
12.5%
2.082191781 1
12.5%
1.657616893 1
12.5%
1.648535565 1
12.5%
1.555555556 1
12.5%
1.286681716 1
12.5%
0.733921816 1
12.5%
0.576875769 1
12.5%

AttPen
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9328776
Minimum0.51694915
Maximum4.4411765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:28.872590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.51694915
5-th percentile0.59978507
Q11.2131583
median1.5319767
Q32.2016741
95-th percentile4.1391085
Maximum4.4411765
Range3.9242273
Interquartile range (IQR)0.98851583

Descriptive statistics

Standard deviation1.3668363
Coefficient of variation (CV)0.70715099
Kurtosis0.30869646
Mean1.9328776
Median Absolute Deviation (MAD)0.49461698
Skewness1.1510991
Sum15.463021
Variance1.8682416
MonotonicityNot monotonic
2023-02-12T10:32:28.951609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.742857143 1
12.5%
4.441176471 1
12.5%
1.583333333 1
12.5%
0.516949153 1
12.5%
1.366336634 1
12.5%
0.753623188 1
12.5%
1.480620155 1
12.5%
3.578125 1
12.5%
ValueCountFrequency (%)
0.516949153 1
12.5%
0.753623188 1
12.5%
1.366336634 1
12.5%
1.480620155 1
12.5%
1.583333333 1
12.5%
1.742857143 1
12.5%
3.578125 1
12.5%
4.441176471 1
12.5%
ValueCountFrequency (%)
4.441176471 1
12.5%
3.578125 1
12.5%
1.742857143 1
12.5%
1.583333333 1
12.5%
1.480620155 1
12.5%
1.366336634 1
12.5%
0.753623188 1
12.5%
0.516949153 1
12.5%

DeadBall
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0288246
Minimum0.91326531
Maximum1.1164384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:29.038628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.91326531
5-th percentile0.94272271
Q10.99935733
median1.042662
Q31.0561622
95-th percentile1.0983949
Maximum1.1164384
Range0.20317305
Interquartile range (IQR)0.056804876

Descriptive statistics

Standard deviation0.059967319
Coefficient of variation (CV)0.058287212
Kurtosis1.4918951
Mean1.0288246
Median Absolute Deviation (MAD)0.032442748
Skewness-0.75684351
Sum8.2305969
Variance0.0035960793
MonotonicityNot monotonic
2023-02-12T10:32:29.120646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.053254438 1
12.5%
1.116438356 1
12.5%
1.037878788 1
12.5%
1.047445255 1
12.5%
1 1
12.5%
1.064885496 1
12.5%
0.997429306 1
12.5%
0.913265306 1
12.5%
ValueCountFrequency (%)
0.913265306 1
12.5%
0.997429306 1
12.5%
1 1
12.5%
1.037878788 1
12.5%
1.047445255 1
12.5%
1.053254438 1
12.5%
1.064885496 1
12.5%
1.116438356 1
12.5%
ValueCountFrequency (%)
1.116438356 1
12.5%
1.064885496 1
12.5%
1.053254438 1
12.5%
1.047445255 1
12.5%
1.037878788 1
12.5%
1 1
12.5%
0.997429306 1
12.5%
0.913265306 1
12.5%

AerialDuel
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2647458
Minimum0.953125
Maximum1.7472527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:29.210668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.953125
5-th percentile0.95580576
Q11.124183
median1.2910259
Q31.3287603
95-th percentile1.6210365
Maximum1.7472527
Range0.79412775
Interquartile range (IQR)0.20457732

Descriptive statistics

Standard deviation0.25306928
Coefficient of variation (CV)0.20009496
Kurtosis1.1561321
Mean1.2647458
Median Absolute Deviation (MAD)0.1039928
Skewness0.65007659
Sum10.117967
Variance0.064044058
MonotonicityNot monotonic
2023-02-12T10:32:29.290687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.386634845 1
12.5%
1.747252747 1
12.5%
1.304147465 1
12.5%
1.178649237 1
12.5%
1.277904328 1
12.5%
0.953125 1
12.5%
0.960784314 1
12.5%
1.309468822 1
12.5%
ValueCountFrequency (%)
0.953125 1
12.5%
0.960784314 1
12.5%
1.178649237 1
12.5%
1.277904328 1
12.5%
1.304147465 1
12.5%
1.309468822 1
12.5%
1.386634845 1
12.5%
1.747252747 1
12.5%
ValueCountFrequency (%)
1.747252747 1
12.5%
1.386634845 1
12.5%
1.309468822 1
12.5%
1.304147465 1
12.5%
1.277904328 1
12.5%
1.178649237 1
12.5%
0.960784314 1
12.5%
0.953125 1
12.5%

Goals
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.353869
Minimum0.71428571
Maximum8.3333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:29.377706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.71428571
5-th percentile1.2342857
Q12.2375
median2.5
Q33.75
95-th percentile7.1666667
Maximum8.3333333
Range7.6190476
Interquartile range (IQR)1.5125

Descriptive statistics

Standard deviation2.345231
Coefficient of variation (CV)0.69926135
Kurtosis2.7512603
Mean3.353869
Median Absolute Deviation (MAD)0.56666667
Skewness1.5557912
Sum26.830952
Variance5.5001084
MonotonicityNot monotonic
2023-02-12T10:32:29.455723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.6 1
12.5%
3.333333333 1
12.5%
5 1
12.5%
2.25 1
12.5%
2.2 1
12.5%
0.714285714 1
12.5%
2.4 1
12.5%
8.333333333 1
12.5%
ValueCountFrequency (%)
0.714285714 1
12.5%
2.2 1
12.5%
2.25 1
12.5%
2.4 1
12.5%
2.6 1
12.5%
3.333333333 1
12.5%
5 1
12.5%
8.333333333 1
12.5%
ValueCountFrequency (%)
8.333333333 1
12.5%
5 1
12.5%
3.333333333 1
12.5%
2.6 1
12.5%
2.4 1
12.5%
2.25 1
12.5%
2.2 1
12.5%
0.714285714 1
12.5%

xG
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2895048
Minimum0.59405941
Maximum4.8095238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.0 B
2023-02-12T10:32:29.547744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.59405941
5-th percentile0.79320383
Q11.2312371
median1.9678571
Q33.0060037
95-th percentile4.4658456
Maximum4.8095238
Range4.2154644
Interquartile range (IQR)1.7747666

Descriptive statistics

Standard deviation1.4410753
Coefficient of variation (CV)0.62942663
Kurtosis-0.35626707
Mean2.2895048
Median Absolute Deviation (MAD)0.78454969
Skewness0.76718969
Sum18.316038
Variance2.0766979
MonotonicityNot monotonic
2023-02-12T10:32:29.622761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.732142857 1
12.5%
3.827586207 1
12.5%
2.285714286 1
12.5%
1.253968254 1
12.5%
1.163043478 1
12.5%
0.594059406 1
12.5%
1.65 1
12.5%
4.80952381 1
12.5%
ValueCountFrequency (%)
0.594059406 1
12.5%
1.163043478 1
12.5%
1.253968254 1
12.5%
1.65 1
12.5%
2.285714286 1
12.5%
2.732142857 1
12.5%
3.827586207 1
12.5%
4.80952381 1
12.5%
ValueCountFrequency (%)
4.80952381 1
12.5%
3.827586207 1
12.5%
2.732142857 1
12.5%
2.285714286 1
12.5%
1.65 1
12.5%
1.253968254 1
12.5%
1.163043478 1
12.5%
0.594059406 1
12.5%

Interactions

2023-02-12T10:32:23.024270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:37.541764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:39.638239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:41.463650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:43.576127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:45.552573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:47.678056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:49.691511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:51.550931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:53.771501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:55.771964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:57.800425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:59.974942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:02.084425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:03.957859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:06.001337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:08.053842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.242096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.296261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.241220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.451780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:18.709296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:20.869784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:23.108290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:37.633786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:39.719257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:41.552671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:43.665148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:45.643594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:47.762075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:49.777530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:51.654986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:53.856522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:55.857985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:57.890444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:00.064960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:02.166443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:04.042876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:06.086356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:08.146863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.331116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.386281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.334242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.547803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:18.800316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:20.961805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:23.181306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:37.711803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:39.795274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:41.630688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:43.740166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:45.724612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:47.838093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:49.849546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:51.865070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:53.929537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:55.939002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:57.969462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:00.142977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:02.241462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:04.117893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:06.161373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:08.230883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.409166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.461298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.416279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.632820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:18.881335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:21.044825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:23.265325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:37.798822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:39.876291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:41.718707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:43.828184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:45.816633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:47.925112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:49.937567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:51.956091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:54.014556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:56.024021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:58.064484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:00.234998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:02.324481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:04.202914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:06.253393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:08.325904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.495184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.548319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.510323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.727842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:18.971355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:21.137845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:23.340342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:37.882841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:39.957311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:41.917754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:43.902201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:45.896651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:48.003129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:50.009584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:52.038110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:54.088572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:56.100037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:58.150503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:00.319018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:02.399497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:04.278929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:06.330411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:08.408923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.571201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.626505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.592347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.813862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-12T10:31:47.599038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:49.617494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:51.477914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:53.689483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:55.696948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:57.725406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:31:59.895921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:01.878376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:03.883840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:05.921307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:07.978825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:10.157072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:12.072211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:14.166202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:16.368763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:18.625279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:20.786765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-12T10:32:22.944252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-12T10:32:29.730784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
TklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntClrDefPenDef3rdTMid3rdTAtt3rdTAttPenDeadBallAerialDuelGoalsxGPlayer
Tkl1.0000.8810.9050.452-0.3330.7860.7620.0480.7140.5950.1190.5710.2620.4760.5480.381-0.262-0.476-0.5950.452-0.143-0.667-0.5711.000
TklW0.8811.0000.7860.286-0.3810.9050.8330.0480.6670.6670.1900.6430.3810.4760.4520.286-0.286-0.476-0.5950.333-0.238-0.714-0.5481.000
Def 3rd0.9050.7861.0000.119-0.3810.7620.857-0.1670.8810.7620.4520.5950.1190.7620.8100.643-0.595-0.762-0.8570.238-0.500-0.786-0.8101.000
Mid 3rd0.4520.2860.1191.0000.0240.167-0.0950.167-0.0950.000-0.6430.1900.333-0.333-0.429-0.4760.5240.3330.2620.3100.548-0.0240.1671.000
Att 3rd-0.333-0.381-0.3810.0241.000-0.690-0.524-0.595-0.238-0.690-0.381-0.7380.024-0.452-0.1670.0950.3570.4520.4050.3330.2380.1900.0951.000
Tkl.10.7860.9050.7620.167-0.6901.0000.9050.1900.6900.8100.2620.8810.3570.5240.4290.262-0.333-0.524-0.6190.000-0.286-0.619-0.4761.000
Att0.7620.8330.857-0.095-0.5240.9051.000-0.0240.9050.7620.4520.7860.3810.6430.7140.619-0.524-0.643-0.762-0.024-0.429-0.619-0.6191.000
Tkl%0.0480.048-0.1670.167-0.5950.190-0.0241.000-0.357-0.095-0.1190.143-0.024-0.238-0.310-0.5000.2380.2380.3100.0950.4760.4290.5951.000
Lost0.7140.6670.881-0.095-0.2380.6900.905-0.3571.0000.6670.4290.6430.3810.6430.8100.810-0.548-0.643-0.786-0.048-0.476-0.619-0.7381.000
Blocks0.5950.6670.7620.000-0.6900.8100.762-0.0950.6671.0000.5950.7860.0950.8330.5240.286-0.690-0.833-0.857-0.262-0.690-0.738-0.7141.000
Sh0.1190.1900.452-0.643-0.3810.2620.452-0.1190.4290.5951.0000.143-0.2620.8810.7140.476-0.976-0.881-0.810-0.190-0.857-0.429-0.5951.000
Pass0.5710.6430.5950.190-0.7380.8810.7860.1430.6430.7860.1431.0000.4760.3810.2380.167-0.214-0.381-0.476-0.405-0.190-0.381-0.3101.000
Int0.2620.3810.1190.3330.0240.3570.381-0.0240.3810.095-0.2620.4761.000-0.238-0.1430.0000.2380.2380.071-0.2620.3570.1670.1671.000
Clr0.4760.4760.762-0.333-0.4520.5240.643-0.2380.6430.8330.8810.381-0.2381.0000.8100.548-0.952-1.000-0.976-0.048-0.905-0.762-0.8571.000
DefPen0.5480.4520.810-0.429-0.1670.4290.714-0.3100.8100.5240.7140.238-0.1430.8101.0000.905-0.786-0.810-0.8570.190-0.690-0.643-0.8101.000
Def3rdT0.3810.2860.643-0.4760.0950.2620.619-0.5000.8100.2860.4760.1670.0000.5480.9051.000-0.548-0.548-0.6430.095-0.524-0.476-0.6901.000
Mid3rdT-0.262-0.286-0.5950.5240.357-0.333-0.5240.238-0.548-0.690-0.976-0.2140.238-0.952-0.786-0.5481.0000.9520.9050.1430.9050.5710.7381.000
Att3rdT-0.476-0.476-0.7620.3330.452-0.524-0.6430.238-0.643-0.833-0.881-0.3810.238-1.000-0.810-0.5480.9521.0000.9760.0480.9050.7620.8571.000
AttPen-0.595-0.595-0.8570.2620.405-0.619-0.7620.310-0.786-0.857-0.810-0.4760.071-0.976-0.857-0.6430.9050.9761.0000.0240.8570.8100.9051.000
DeadBall0.4520.3330.2380.3100.3330.000-0.0240.095-0.048-0.262-0.190-0.405-0.262-0.0480.1900.0950.1430.0480.0241.0000.190-0.286-0.1431.000
AerialDuel-0.143-0.238-0.5000.5480.238-0.286-0.4290.476-0.476-0.690-0.857-0.1900.357-0.905-0.690-0.5240.9050.9050.8570.1901.0000.7140.8331.000
Goals-0.667-0.714-0.786-0.0240.190-0.619-0.6190.429-0.619-0.738-0.429-0.3810.167-0.762-0.643-0.4760.5710.7620.810-0.2860.7141.0000.9291.000
xG-0.571-0.548-0.8100.1670.095-0.476-0.6190.595-0.738-0.714-0.595-0.3100.167-0.857-0.810-0.6900.7380.8570.905-0.1430.8330.9291.0001.000
Player1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-02-12T10:32:25.103741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-12T10:32:25.387804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PlayerTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntClrDefPenDef3rdTMid3rdTAtt3rdTAttPenDeadBallAerialDuelGoalsxG
0England1.0546880.9342110.9016391.2400001.0588240.9285710.7500001.2375690.6486490.8809520.4285711.0317460.7608700.6621620.7932580.9786101.5276041.6576171.7428571.0532541.3866352.6000002.732143
1France0.8833330.8554220.4761901.6896552.4285710.5869570.5531911.0613500.5208330.5432100.2916670.6491230.8115940.3706290.4218750.5593221.6760423.2662344.4411761.1164381.7472533.3333333.827586
2Germany0.8782610.6849320.7857140.8490572.0000000.5681820.6352940.8938220.7073170.8028171.0588240.7222220.8064520.7540980.9084511.1113511.1444441.6485361.5833331.0378791.3041475.0000002.285714
3Italy1.4197531.6000001.8888891.1176470.8181821.6896551.4375001.1766001.2285711.4528302.1000001.3023260.8800001.3555561.3236361.0122320.8091480.5768760.5169491.0474451.1786492.2500001.253968
4Netherlands1.1741941.1875001.0000001.4074071.5000001.0289861.1920000.8641301.3928571.0897440.6000001.3207551.1529410.9423080.8459661.0675071.2845161.5555561.3663371.0000001.2779042.2000001.163043
5Norway0.9157890.9677420.9800000.6923081.8333330.7727270.8658540.8919930.9736840.8961041.3333330.7627120.7462691.2906981.4456931.3606560.9408330.7339220.7536231.0648850.9531250.7142860.594059
6Sweden0.7359550.6964290.4895831.0149251.0666670.5882350.5878381.0017420.5873020.9156631.2142860.8550720.6947370.9477120.7765960.6864410.9806711.2866821.4806200.9974290.9607842.4000001.650000
7USA0.6536310.7254900.4622640.8387101.4545450.7321430.6692311.0928070.6216220.8309860.7000000.8823530.9186050.5326090.5968820.7411601.3363972.0821923.5781250.9132651.3094698.3333334.809524
PlayerTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntClrDefPenDef3rdTMid3rdTAtt3rdTAttPenDeadBallAerialDuelGoalsxG
0England1.0546880.9342110.9016391.2400001.0588240.9285710.7500001.2375690.6486490.8809520.4285711.0317460.7608700.6621620.7932580.9786101.5276041.6576171.7428571.0532541.3866352.6000002.732143
1France0.8833330.8554220.4761901.6896552.4285710.5869570.5531911.0613500.5208330.5432100.2916670.6491230.8115940.3706290.4218750.5593221.6760423.2662344.4411761.1164381.7472533.3333333.827586
2Germany0.8782610.6849320.7857140.8490572.0000000.5681820.6352940.8938220.7073170.8028171.0588240.7222220.8064520.7540980.9084511.1113511.1444441.6485361.5833331.0378791.3041475.0000002.285714
3Italy1.4197531.6000001.8888891.1176470.8181821.6896551.4375001.1766001.2285711.4528302.1000001.3023260.8800001.3555561.3236361.0122320.8091480.5768760.5169491.0474451.1786492.2500001.253968
4Netherlands1.1741941.1875001.0000001.4074071.5000001.0289861.1920000.8641301.3928571.0897440.6000001.3207551.1529410.9423080.8459661.0675071.2845161.5555561.3663371.0000001.2779042.2000001.163043
5Norway0.9157890.9677420.9800000.6923081.8333330.7727270.8658540.8919930.9736840.8961041.3333330.7627120.7462691.2906981.4456931.3606560.9408330.7339220.7536231.0648850.9531250.7142860.594059
6Sweden0.7359550.6964290.4895831.0149251.0666670.5882350.5878381.0017420.5873020.9156631.2142860.8550720.6947370.9477120.7765960.6864410.9806711.2866821.4806200.9974290.9607842.4000001.650000
7USA0.6536310.7254900.4622640.8387101.4545450.7321430.6692311.0928070.6216220.8309860.7000000.8823530.9186050.5326090.5968820.7411601.3363972.0821923.5781250.9132651.3094698.3333334.809524